Computing Probabilistic Controlled Invariant Sets
Yulong Gao, Karl Henrik Johansson, Lihua Xie
Abstract
This article investigates stochastic invariance for control systems through probabilistic controlled invariant sets (PCISs). As a natural complement to robust controlled invariant sets (RCISs), we propose finite-, and infinite-horizon PCISs, and explore their relation to RICSs. We design iterative algorithms to compute the PCIS within a given set. For systems with discrete spaces, the computations of the finite-, and infinite-horizon PCISs at each iteration are based on linear programming, and mixed integer linear programming, respectively. The algorithms are computationally tractable, and terminate in a finite number of steps. For systems with continuous spaces, we show how to discretize the spaces, and prove the convergence of the approximation when computing the finite-horizon PCISs. In addition, it is shown that an infinite-horizon PCIS can be computed by the stochastic backward reachable set from the RCIS contained in it. These PCIS algorithms are applicable to practical control systems. Simulations are given to illustrate the effectiveness of the theoretical results for motion planning.